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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika SMATIKA Jurnal Ilmiah Universitas Batanghari Jambi SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Nasional Komputasi dan Teknologi Informasi JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Pengabdian Masyarakat Asia Jurnal Pengabdian Dharma Wacana JAPI: Jurnal Akses Pengabdian Indonesia Journal of Internet and Software Engineering Journal of Computing Theories and Applications Journal of Information System and Application Development Smatika Jurnal : STIKI Informatika Jurnal
Anis Zubair, Anis
Sistem Informasi, Fakultas Teknologi Informasi, Universitas Merdeka Malang,
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Transportation Other

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Classifying School Scope Using Deep Neural Networks Based on Students' Surrounding Living Environments Mochammad Daffa Putra Karyudi; Anis Zubair
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11739

Abstract

This research investigates school scope classification using Deep Neural Networks (DNN), focusing on students living environments and educational opportunities. By addressing the interplay of socioeconomic and educational factors, the study aims to develop an analytical framework for understanding how environmental contexts shape academic trajectories. The research provides a nuanced understanding of the importance of features in educational classification by developing DNN models based on Spearman's Rank Correlation Coefficient (SRCC). The methodology employs machine learning techniques, integrating data wrangling, exploratory analysis, and multiple DNN models with K-fold cross-validation. The study analyzes 677 student records from two schools. The research examined multiple model configurations. Results show that the 'All Data' model achieved 83.08% accuracy, the 'Top 5' model 81.54%, and the 'Non-Top 5' model 79.23%. The SRCC-based approach revealed that while top correlated features are important, additional variables significantly contribute to model performance. The study highlights the profound impact of family background, social environment, and educational contexts on school selection. Furthermore, it demonstrates DNN's capability to uncover intricate, non-linear relationships, offering actionable insights for policymakers to leverage machine learning's potential in developing targeted educational strategies.
Co-Authors AA Sudharmawan, AA Adha, Muhammad Nibras Aditya Hidayat Pratama Agung Adha, Muhammad Nibras ambu may tida Anak Agung Gede Asri Samsiar Ilmananda Bambang Nurdewanto Bekti Prihatiningsih Edward Roland Sitompul Elta Sonalitha Elta Sonalitha Sonalitha Fridayanti, Dinda Ahsana Zahra Haris Satriyawan Hasugian, Elsa Dame Ripka Hoar, Wilhelmina Sonya Indana, Luthfi Iqbal Baghis Kenvin Kristuadji, Yesaya Arya Danar Lailil Muflikhah Mochammad Daffa Putra Karyudi Nanis Hairunisya Putri, Sephia Dwi Arma Rahmatina Hidayati Ramadhan, Viry Puspaning Rauda Umamit Rijanto, Jonathan Billy Sadili, Ach Savsavubun, Valentinus Viry Puspaning Ramadhan Wulandari, Fadila Yuliati, Ninik Catur Endah